Visualization of class A GPCR oligomerization by image-based fluorescence fluctuation spectroscopy
Bibliographic record
Abstract
Abstract G protein-coupled receptors (GPCRs) represent the largest class of cell surface receptors conveying extracellular information into intracellular signals. Many GPCRs have been shown to be able to oligomerize and it is firmly established that Class C GPCRs (e.g. metabotropic glutamate receptors) function as obligate dimers. However, the oligomerization capability of the larger Class A GPCRs (e.g. comprising the β-adrenergic receptors (β-ARs)) is still, despite decades of research, highly debated. Here we assess the oligomerization behavior of three prototypical Class A GPCRs, the β 1 -ARs, β 2 -ARs, and muscarinic M 2 Rs in single, intact cells. We combine two image correlation spectroscopy methods based on molecular brightness, i.e. the analysis of fluorescence fluctuations over space and over time, and thereby provide an assay able to robustly and precisely quantify the degree of oligomerization of GPCRs. In addition, we provide a comparison between two labelling strategies, namely C-terminally-attached fluorescent proteins and N-terminally-attached SNAP-tags, in order to rule out effects arising from potential fluorescent protein-driven oligomerization. The degree of GPCR oligomerization is expressed with respect to a set of previously reported as well as newly established monomeric or dimeric control constructs. Our data reveal that all three prototypical GPRCs studied display, under unstimulated conditions, a prevalently monomeric fingerprint. Only the β 2 -AR shows a slight degree of oligomerization. From a methodological point of view, our study suggests three key aspects. First, the combination of two image correlation spectroscopy methods allows addressing cells transiently expressing high concentrations of membrane receptors, far from the single molecule regime, at a density where the kinetic equilibrium should favor dimers and higher-order oligomers. Second, our methodological approach, allows to selectively target cell membrane regions devoid of artificial oligomerization hot-spots (such as vesicles). Third, our data suggest that the β 1 -AR appears to be a superior monomeric control than the widely used membrane protein CD86. Taken together, we suggest that our combined image correlation spectroscopy method is a powerful approach to assess the oligomerization behavior of GPCRs in intact cells at high expression levels.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".